Introduction to Data Science Training Course

This is a 5-day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

This instructor-led live training in Romania (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led live training Romania (online or on-site) is targeted at data scientists who wish to utilize the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows within a single platform.

By the end of this training, participants will be able to:

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in Romania (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

By the end of this training, participants will be able to:

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led live training in Romania (online or on-site) is designed for beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

Upon completing this training, participants will acquire a practical, real-world grasp of Data Science, including its associated technologies, methodologies, and tools.

The program offers ample opportunities to apply this knowledge through practical, hands-on exercises. Class dynamics are enriched by group collaboration and direct feedback from the instructor.

The curriculum begins with fundamental Data Science concepts before advancing to the specific tools and methodologies employed in the field.

Target Audience

• Software Developers
• Technical Analysts
• IT Consultants

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• For customized training options, please contact us to arrange a session.

Big data refers to data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

This course is designed for marketing and sales professionals aiming to deepen their understanding of data science applications within these fields. It offers a comprehensive exploration of various data science techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Differentiating Marketing and Sales - What sets them apart?

In simple terms, sales focuses on individuals or small groups, whereas marketing targets broader audiences or the general public. Marketing involves researching customer needs, developing innovative products, promoting them through advertising, and building awareness. Essentially, marketing generates leads or prospects. Once the product is launched, the sales team's role is to persuade these prospects to make a purchase. Sales converts leads into orders, focusing on shorter-term goals, while marketing emphasizes long-term strategy and brand building.

This instructor-led, live training in Romania (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". It guides participants through the creation of a sample data science project built on the Jupyter ecosystem.

By the end of this training, participants will be able to:

• Install and configure Jupyter, including setting up and integrating a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems like Apache Spark, all through the Jupyter interface.

This instructor-led, live training in Romania (online or onsite) is aimed at data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

By the end of this training, participants will be able to:

• Learn about data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation, enabling you to uncover hidden potential within your data, extract new insights, or forecast future trends. Boasting over 1000 modules, numerous ready-to-execute examples, a broad spectrum of integrated tools, and an extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for both data scientists and business analysts.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to become familiar with the platform, learn to utilize it more efficiently, and master the creation of clear, comprehensive reports based on KNIME workflows.

This instructor-led, live training (available online or onsite) is designed for data professionals seeking to leverage KNIME to address complex business challenges.

The training targets individuals with no programming background who wish to utilize state-of-the-art tools to implement analytics scenarios.

Upon completion of this training, participants will be capable of:

• Installing and configuring KNIME.
• Developing Data Science scenarios
• Training, testing, and validating models
• Implementing the end-to-end value chain for data science models

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation in a live laboratory environment.

Course Customization Options

• To request customized training for this course or to obtain more information about this program, please contact us to arrange details.

The training begins with an exploration of MATLAB’s core fundamentals, highlighting its role as both a programming language and a comprehensive platform. This section introduces essential MATLAB syntax, the manipulation of arrays and matrices, data visualization techniques, script development, and the foundational concepts of object-oriented programming.

During the second phase, the focus shifts to leveraging MATLAB for data mining, machine learning, and predictive analytics. To illustrate MATLAB’s unique advantages and capabilities, we compare its workflow against other common tools such as spreadsheets, C, C++, and Visual Basic, providing participants with a clear, practical understanding of why MATLAB is often the preferred choice for complex data tasks.

In the final segment, participants learn strategies to streamline their workflows by automating data processing and the generation of comprehensive reports.

Throughout the course, participants will apply learned concepts through hands-on exercises in a lab environment. By the conclusion of the training, attendees will possess a robust understanding of MATLAB’s capabilities, enabling them to tackle real-world data science challenges and automate their professional tasks effectively.

Progress will be monitored through assessments conducted throughout the course.

Format of the Course

• The course combines theoretical instruction with practical exercises, including case study discussions, code analysis, and hands-on implementation.

Note

• Practice sessions utilize pre-arranged sample data report templates. If you have specific custom requirements, please contact us to make arrangements.

This instructor-led, live training in Romania (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Romania (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

By the end of this training, participants will be able to:

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

This instructor-led live training in Romania (online or onsite) is designed for data scientists and developers who want to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms like XGBoost and cuML.

By the end of this training, participants will be able to:

• Set up the required development environment to build data models with NVIDIA RAPIDS.
• Understand the features, components, and advantages of RAPIDS.
• Leverage GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
• Learn how to perform machine learning tasks using XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis using cuXfilter and cuGraph.